How to use from
Pi
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "osirisbrain/OsirisCortex-v7c-MLX"
Configure the model in Pi
# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "osirisbrain/OsirisCortex-v7c-MLX"
        }
      ]
    }
  }
}
Run Pi
# Start Pi in your project directory:
pi
Quick Links

OsirisCortex-v7c-MLX (Censored)

The Cortex v7c — Osiris's sovereign reasoning brain (censored variant). Standard safety guardrails intact. Runs natively on Apple Silicon via MLX Metal.

Architecture

  • Base Model: Qwen3.5-9B (9 billion parameters)
  • Modification: None — original base model with safety alignment preserved
  • Format: MLX 8-bit quantized (Apple Silicon native)
  • Size: ~10 GB
  • Speed: ~60-80 tokens/sec on M2 Pro (MLX Metal)
  • Quality: Near-lossless vs FP16 (~1-2% degradation)

Why 8-bit

  • Spanish coherence: 4-bit quantization degrades non-English languages significantly
  • Conversational quality: 8-bit produces more natural, coherent dialogue
  • Safety: Standard Qwen3.5 alignment — no abliteration

Usage

from mlx_lm import load, generate

model, tokenizer = load("osirisbrain/OsirisCortex-v7c-MLX")
prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": "¿Cómo estás?"}],
    add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, max_tokens=2048)

Credits

Quantized by mlx-community. Original model: Qwen/Qwen3.5-9B by Alibaba. Sovereign rebrand by OsirisBrain.

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8-bit

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